


import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= [
		    '''encoder.version''',
		    '''decoder.version''',
		    '''model.encoder.version''',
		    '''model.decoder.version''',
		    '''decoder.output_projection.weight''',
		    '''_float_tensor''',
		    '''encoder.embed_positions._float_tensor''',
		    '''decoder.embed_positions._float_tensor''',
		]
		for k in ignore_keys:
				state_dict.pop(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__	,						lowercase__				= emb.weight.shape
		lowercase__				= nn.Linear(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							bias=SCREAMING_SNAKE_CASE	)
		lowercase__				= emb.weight.data
		return lin_layer
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None	):


		"""simple docstring"""




		lowercase__				= {}
		for old_key in state_dict.keys():
				lowercase__				= old_key
				if "moe_layer.experts." in key:
						if expert_idx is not None:
								lowercase__				= key.replace('''moe_layer.experts.0'''		,							f'ffn.experts.expert_{expert_idx}'	)
						else:
								lowercase__				= key.replace('''moe_layer.experts.'''		,							'''ffn.experts.expert_'''	)
				if "gate" in key:
						lowercase__				= key.replace('''.moe_layer.gate.wg'''		,							'''.ffn.router.classifier'''	)
				if "fc2" and "experts" not in key:
						lowercase__				= key.replace('''.fc2.'''		,							'''.ffn.fc2.'''	)
				if "fc1" and "experts" not in key:
						lowercase__				= key.replace('''.fc1.'''		,							'''.ffn.fc1.'''	)
				if ".encoder_attn." in key:
						lowercase__				= key.replace('''.encoder_attn.'''		,							'''.cross_attention.'''	)
				if "encoder_attn_layer_norm" in key:
						lowercase__				= key.replace('''encoder_attn_layer_norm'''		,							'''cross_attention_layer_norm'''	)
				if "final_layer_norm" in key:
						lowercase__				= key.replace('''final_layer_norm'''		,							'''ff_layer_norm'''	)
				lowercase__				= state_dict[old_key]
		return new_dict
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = WEIGHTS_NAME	):


		"""simple docstring"""




		lowercase__				= []
		lowercase__				= 0
		os.makedirs(SCREAMING_SNAKE_CASE		,							exist_ok=SCREAMING_SNAKE_CASE	)

		for expert in range(SCREAMING_SNAKE_CASE	):
				lowercase__				= switch_checkpoint_path + f'-rank-{expert}.pt'
				if os.path.isfile(SCREAMING_SNAKE_CASE	):
						lowercase__				= torch.load(SCREAMING_SNAKE_CASE	)['''model''']
						remove_ignore_keys_(SCREAMING_SNAKE_CASE	)
						lowercase__				= rename_fairseq_keys(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
						lowercase__				= os.path.join(
						    SCREAMING_SNAKE_CASE		,							weights_name.replace('''.bin'''		,							f'-{len(SCREAMING_SNAKE_CASE	)+1:05d}-of-???.bin'	)	)
						torch.save(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
						sharded_state_dicts.append(expert_state.keys()	)
						total_size += sum([value.numel() for key, value in expert_state.items()]	) * dtype_byte_size(
						    expert_state[list(SCREAMING_SNAKE_CASE	)[0]].dtype	)

    # Add the last block
		lowercase__				= os.path.join(SCREAMING_SNAKE_CASE		,							weights_name.replace('''.bin'''		,							f'-{len(SCREAMING_SNAKE_CASE	)+1:05d}-of-???.bin'	)	)
		lowercase__				= torch.load(switch_checkpoint_path + '''-shared.pt'''	)['''model''']
		remove_ignore_keys_(SCREAMING_SNAKE_CASE	)
		lowercase__				= rename_fairseq_keys(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		lowercase__				= shared_weights['''decoder.embed_tokens.weight''']
		sharded_state_dicts.append(shared_weights.keys()	)

		# If we only have the shared weights (dummy model/experts saved on the same file)
		if len(SCREAMING_SNAKE_CASE	) == 1:
				lowercase__				= os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
				torch.save(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
				return {weights_name: sharded_state_dicts[0]}, None
		else:
				torch.save(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		# Otherwise, let's build the index
		lowercase__				= {}
		for idx, shard in enumerate(SCREAMING_SNAKE_CASE	):
				lowercase__				= weights_name.replace('''.bin'''		,							f'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE	):05d}.bin'	)
				lowercase__				= os.path.join(SCREAMING_SNAKE_CASE		,							weights_name.replace('''.bin'''		,							f'-{idx+1:05d}-of-???.bin'	)	)
				os.rename(SCREAMING_SNAKE_CASE		,							os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)	)
				for key in shard:
						lowercase__				= shard_file

    # Add the metadata
		lowercase__				= {'''total_size''': total_size}
		lowercase__				= {'''metadata''': metadata, '''weight_map''': weight_map}

		with open(os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)		,							'''w'''		,							encoding='''utf-8'''	) as f:
				lowercase__				= json.dumps(SCREAMING_SNAKE_CASE		,							indent=2		,							sort_keys=SCREAMING_SNAKE_CASE	) + '''\n'''
				f.write(SCREAMING_SNAKE_CASE	)

		return metadata, index


if __name__ == "__main__":
			lowerCAmelCase			=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--nllb_moe_checkpoint_path',
			    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
			    type=str,
			    required=False,
			    help='Path to a directory containing a folder per layer. Follows the original Google format.',
			)
			parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
			    type=str,
			    required=False,
			    help='Path to the output pytorch model.',
			)
			lowerCAmelCase			=				parser.parse_args()
			lowerCAmelCase,		lowerCAmelCase			=				shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    128,
			    args.dtype,
			)

			lowerCAmelCase			=				NllbMoeConfig.from_pretrained(
			    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
			)
			config.save_pretrained(args.pytorch_dump_folder_path)
			lowerCAmelCase			=				NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print('Done')
			model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor



class 	__a	(   unittest.TestCase						):






       def __init__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=7							,   UpperCAmelCase_			:				List[Any]=3							,   UpperCAmelCase_			:				Optional[int]=30							,   UpperCAmelCase_			:				Any=400							,   UpperCAmelCase_			:				Any=True							,   UpperCAmelCase_			:				Dict=None							,   UpperCAmelCase_			:				Dict=0.9							,   UpperCAmelCase_			:				Tuple=None							,   UpperCAmelCase_			:				Tuple=True							,   UpperCAmelCase_			:				Optional[int]=[0.5, 0.5, 0.5]							,   UpperCAmelCase_			:				Union[str, Any]=[0.5, 0.5, 0.5]							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		size if size is not None else {"shortest_edge": 30}
              UpperCamelCase       			=		crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase       			=		parent
              UpperCamelCase       			=		batch_size
              UpperCamelCase       			=		num_channels
              UpperCamelCase       			=		min_resolution
              UpperCamelCase       			=		max_resolution
              UpperCamelCase       			=		do_resize_and_center_crop
              UpperCamelCase       			=		size
              UpperCamelCase       			=		crop_pct
              UpperCamelCase       			=		crop_size
              UpperCamelCase       			=		do_normalize
              UpperCamelCase       			=		image_mean
              UpperCamelCase       			=		image_std






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }





@require_torch
@require_vision
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       List[Any]				=      PoolFormerImageProcessor if is_vision_available() else None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	str:







              """simple docstring"""
              UpperCamelCase       			=		PoolFormerImageProcessingTester(self      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	Optional[Any]:







              """simple docstring"""
              return self.image_processor_tester.prepare_image_processor_dict()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_resize_and_center_crop"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "size"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "crop_pct"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "do_normalize"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_mean"      )      )
              self.assertTrue(hasattr(UpperCAmelCase_							,   "image_std"      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 30}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 30, "width": 30}      )

              UpperCamelCase       			=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84      )
              self.assertEqual(image_processor.size							,   {"shortest_edge": 42}      )
              self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Any:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PIL images
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   Image.Image      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values

              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[str]:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random numpy tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   numpify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   np.ndarray      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str      )->	str:







              """simple docstring"""
              # Initialize image_processing
              UpperCamelCase       			=		self.image_processing_class(**self.image_processor_dict      )
              # create random PyTorch tensors
              UpperCamelCase       			=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=UpperCAmelCase_							,   torchify=UpperCAmelCase_      )
              for image in image_inputs:
                     self.assertIsInstance(UpperCAmelCase_							,   torch.Tensor      )

              # Test not batched input
              UpperCamelCase       			=		image_processing(image_inputs[0]							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )

              # Test batched
              UpperCamelCase       			=		image_processing(UpperCAmelCase_							,   return_tensors="pt"      ).pixel_values
              self.assertEqual(
                  encoded_images.shape							,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )							,   )





